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**umi-pipeline-nf** is based on a [snakemake pipeline](https://github.com/nanoporetech/pipeline-umi-amplicon) provided by [Oxford Nanopore Technologies (ONT)](https://nanoporetech.com/). To increase efficiency and usability the pipeline was transferred to [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation simple and results highly reproducible.
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**Umi-pipeline-nf** creates highly accurate single-molecule consensus sequences for unique molecular identifier (UMI)-tagged amplicon data.
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The pipeline can be run for the whole fastq_pass folder of your nanopore run and, per default, outputs the aligned consensus sequences of each UMI cluster in bam file. The optional variant calling creates a vcf file for all variants that are found in the consensus sequences.
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umi-pipeline-nf is based on the snakemake [ONT UMI analysis pipeline](https://github.com/nanoporetech/pipeline-umi-amplicon) (workflow originally developed by [Karst et al, Nat Biotechnol 18:165–169, 2021](https://www.nature.com/articles/s41592-020-01041-y)). We transferred the pipeline to [Nextflow](https://www.nextflow.io) and included [additional functionalities](#main-adaptations).
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## Overview
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`umi-pipeline-nf` creates highly accurate single-molecule consensus sequences based on amplicon data tagged by unique molecular identifiers (UMIs). The pipeline can be run for the whole fastq_pass folder of your nanopore run and per default, the output are the aligned consensus sequences in bam file format.
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Additional flags can be set to perform a variant calling ( [freebayes](https://github.com/freebayes/freebayes), [lofreq](http://csb5.github.io/lofreq/) or [mutserve](https://mitoverse.readthedocs.io/mutserve/mutserve/) )
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## Workflow
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> See the [output documentation](docs/output.md) for more details of the results.
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1. Input reads are aligned against a reference genome.
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2. The flanking UMI sequences of all reads are extracted.
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3. The extracted UMIs are used to cluster the reads.
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4. Per cluster, highly accurate consensus sequences are created.
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5. The consensus sequences are aligned against the reference sequenced.
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6. An optional variant calling step can be performed.
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> See the [output documentation](docs/output.md) for a detailed overview of the pipeline and its output files.
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## Main Adaptations
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* It comes with docker containers making **installation simple, portable** and **results highly reproducible**.
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* The pipeline is **optimized for parallelization**.
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* Read filtering strategy per UMI cluster was adapted to **preserve the highest quality reads**.
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***Three commonly used variant callers** ([freebayes](https://github.com/freebayes/freebayes), [lofreq](http://csb5.github.io/lofreq/) or [mutserve](https://mitoverse.readthedocs.io/mutserve/mutserve/)) are supported by the pipeline.
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* The raw reads can be optionally **subsampled**.
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* The raw reads can be **filtered by read length and quality**.
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> See the [usage documentation](docs/usage.md) for all of the available parameters of the pipeline.
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## Quick Start
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2. Download the pipeline and test it on a minimal dataset with a single command
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```bash
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nextflow run AmstlerStephan/umi-pipeline-nf -profile test,docker
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nextflow run genepi/umi-pipeline-nf -profile test,docker
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```
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3. Start running your own analysis!
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3.1 Download and adapt the config/custom.config with paths to your data (relative and absolute paths possible)
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```bash
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nextflow run AmstlerStephan/umi-pipeline-nf -r main -c <custom.config> -profile docker
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nextflow run genepi/umi-pipeline-nf -r main -c <custom.config> -profile docker
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```
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> See the [usage documentation](docs/usage.md) for all of the available options when running the pipeline.
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### Credits
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These scripts were originally written for use by [GENEPI](https://genepi.i-med.ac.at/), by ([@StephanAmstler](https://github.com/AmstlerStephan)).
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The pipeline was written by ([@StephanAmstler](https://github.com/AmstlerStephan)).
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